Multiple studies have shown that discrete messages enhance performance as agents develop the language to communicate with each other [1]}, [2]}, [3]}. On the other hand, language capabilities to succinctly describe images in only a few words have inspired several studies in representation learning [4]}, [5]} to use discrete stochastic states. In our communication protocol, we can see the duality of these ideas as we can train stochastic states on unsupervised loss to reason about the current state of the agent and at the same time use it to communicate agent's current surroundings.
